Application of Artificial Neural Networks to Classify Water Quality of the Yellow River
作者： Li-hua Chen , Xiao-yun Zhang
DOI: 10.1007/978-3-540-88914-4_3
关键词: Sampling (statistics) 、 Chemical oxygen demand 、 Soil science 、 Uniform design 、 Artificial neural network 、 Chemical measurement 、 Sampling time 、 Environmental science 、 Water quality
摘要: Within the period from 2003 to 2005 (high water, normal water and low water) 63 samples are collected measurement of 10 chemical variables Yellow River Gansu period, carried out. These dissolved oxygen (DO), demand (COD), non-ion ammonia (NH x ), volatilization Hydroxybenzene (OH), cyanide (CN), As, Hg, Cr6 + , Pb, Cd. For handling results all measurements different chemoinformatics methods employed: (i) The basic statistical that uniform design is employed determinate data set according quality standard, (ii) MLP neural network (BP) Probabilistic networks (PNN) used classify sampling site time. correlation between classes sought. model built, these models could quickly, completely accurately River.
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